Abstract
We formulate a continuous-time price discovery model in which the price discovery measure varies (stochastically) at daily frequency. We estimate daily measures of price discovery using a kernel-based OLS estimator instead of running separate daily VECM regressions as standard in the literature. Our method outperforms the standard daily VECM in finite samples. We illustrate our theoretical findings with 10 actively traded stocks in the U.S. from 2007 to 2013.
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